The subject matter disclosed herein relates to tomographic reconstruction, and in particular to the use of deep learning techniques to reconstruct data, such as projection or other scan-type data, into diagnostically or clinically useful images, including cross-sectional images and/or volumetric representations.
Non-invasive imaging technologies allow images of the internal structures or features of a patient/object to be obtained without performing an invasive procedure on the patient/object. In particular, such non-invasive imaging technologies rely on various physical principles (such as the differential transmission of X-rays through the target volume, the reflection of acoustic waves within the volume, the paramagnetic properties of different tissues and materials within the volume, the breakdown of targeted radionuclides within the body, and so forth) to acquire data and to construct images or otherwise represent the observed internal features of the patient/object.
All reconstruction algorithms are subject to various trade-offs, such as between computational efficiency, patient dose, scanning speed, image quality, and artifacts. By way of example, machining learning architectures based on convolutional neural networks (CNN) have set benchmarks in a number of pattern recognition, image processing, detection and classification tasks. However, in a tomographic reconstruction context, a CNN may be unsuitable as conventionally implemented. In particular, a CNN is typically constructed based on the principles of local connectivity and weights sharing. Weight sharing (i.e., space-invariant convolution) dramatically reduces the number of free parameters of the network, thus lowering the training time and memory requirements for the network. However, although successful in many computer vision problems, the principle of weights sharing also inherently limits the network to be space-invariant, i.e., features to be detected regardless of their position in the visual field, thus constituting the property of translation invariance. In other words, the convolution operation in CNN is typically implemented by Fourier filters, which is inherently translation invariant. Although this achieves good results on many computer vision problems, it becomes unsuitable for many space-variant tasks such image restoration and/or reconstruction with a space-variant point spread function (PSF). On the other hand, a fully connected deep neural network is not computationally feasible for most high dimensional problems (e.g., image reconstruction).